Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models

TL;DR

This paper introduces a novel quantum-inspired graph neural network with a CP decomposition layer, combined with ensemble models, to improve detection of illicit blockchain transactions in anti-money laundering efforts, achieving promising results.

Contribution

It presents a new quantum-inspired GNN architecture with a CP layer and ensemble approach, advancing blockchain analysis for financial security.

Findings

Achieved an F2 score of 74.8% in fraud detection.

Demonstrated the effectiveness of quantum-inspired techniques over classical methods.

Showcased the potential for broader adoption of quantum-inspired algorithms in finance.

Abstract

In the rapidly evolving domain of financial technology, the detection of illicit transactions within blockchain networks remains a critical challenge, necessitating robust and innovative solutions. This work proposes a novel approach by combining Quantum Inspired Graph Neural Networks (QI-GNN) with flexibility of choice of an Ensemble Model using QBoost or a classic model such as Random Forrest Classifier. This system is tailored specifically for blockchain network analysis in anti-money laundering (AML) efforts. Our methodology to design this system incorporates a novel component, a Canonical Polyadic (CP) decomposition layer within the graph neural network framework, enhancing its capability to process and analyze complex data structures efficiently. Our technical approach has undergone rigorous evaluation against classical machine learning implementations, achieving an F2 score of 74.8% in detecting fraudulent transactions. These results highlight the potential of quantum-inspired techniques, supplemented by the structural advancements of the CP layer, to not only match but potentially exceed traditional methods in complex network analysis for financial security. The findings advocate for a broader adoption and further exploration of quantum-inspired algorithms within the financial sector to effectively combat fraud.